LbfgsPoissonRegressionTrainer Class

  • Reference

Definition

Namespace:
Microsoft.ML.Trainers
Assembly:
Microsoft.ML.StandardTrainers.dll
Package:
Microsoft.ML v3.0.1
Package:
Microsoft.ML v1.0.0
Package:
Microsoft.ML v1.1.0
Package:
Microsoft.ML v1.2.0
Package:
Microsoft.ML v1.3.1
Package:
Microsoft.ML v1.4.0
Package:
Microsoft.ML v1.5.5
Package:
Microsoft.ML v1.6.0
Package:
Microsoft.ML v1.7.0
Package:
Microsoft.ML v2.0.0

The IEstimator<TTransformer> for training a Poisson regression model.

public sealed class LbfgsPoissonRegressionTrainer : Microsoft.ML.Trainers.LbfgsTrainerBase<Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.Options,Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.PoissonRegressionModelParameters>,Microsoft.ML.Trainers.PoissonRegressionModelParameters>
type LbfgsPoissonRegressionTrainer = class
    inherit LbfgsTrainerBase<LbfgsPoissonRegressionTrainer.Options, RegressionPredictionTransformer<PoissonRegressionModelParameters>, PoissonRegressionModelParameters>
Public NotInheritable Class LbfgsPoissonRegressionTrainer
Inherits LbfgsTrainerBase(Of LbfgsPoissonRegressionTrainer.Options, RegressionPredictionTransformer(Of PoissonRegressionModelParameters), PoissonRegressionModelParameters)
Inheritance
Object
TrainerEstimatorBase<TTransformer,TModel>
LbfgsTrainerBase<LbfgsPoissonRegressionTrainer.Options,RegressionPredictionTransformer<PoissonRegressionModelParameters>,PoissonRegressionModelParameters>
LbfgsPoissonRegressionTrainer

Remarks

To create this trainer, use LbfgsPoissonRegression or LbfgsPoissonRegression(Options).

Input and Output Columns

The input label column data must be Single. The input features column data must be a known-sized vector of Single.

This trainer outputs the following columns:

Output Column Name Column Type Description
Score Single The unbounded score that was predicted by the model.

Trainer Characteristics
Machine learning task Regression
Is normalization required? Yes
Is caching required? No
Required NuGet in addition to Microsoft.ML None
Exportable to ONNX Yes

Training Algorithm Details

Poisson regression is a parameterized regression method. It assumes that the log of the conditional mean of the dependent variable follows a linear function of the dependent variables. Assuming that the dependent variable follows a Poisson distribution, the regression parameters can be estimated by maximizing the likelihood of the obtained observations.

Check the See Also section for links to usage examples.

Fields

FeatureColumn

The feature column that the trainer expects.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
LabelColumn

The label column that the trainer expects. Can be null, which indicates that label is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
WeightColumn

The weight column that the trainer expects. Can be null, which indicates that weight is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Properties

Info (Inherited from LbfgsTrainerBase<TOptions,TTransformer,TModel>)

Methods

Fit(IDataView)

Trains and returns a ITransformer.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
Fit(IDataView, LinearModelParameters)

Continues the training of a LbfgsPoissonRegressionTrainer using an already trained linearModel and returns a RegressionPredictionTransformer<TModel>.
GetOutputSchema(SchemaShape) (Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.
WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.

Applies to

See also